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Informationen zum Autor Gustavo Camps-Valls was born in Valencia, Spain in 1972, and received a B.Sc. degree in Physics (1996), a B.Sc. degree in Electronics Engineering (1998), and a Ph.D. degree in Physics (2002) from the Universitat de Valencia. He is currently an associate professor in the Department of Electronics Engineering at the Universitat de Valencia, where he teaches electronics, advanced time series processing, machine learning for remote sensing and digital signal processing. His research interests are tied to the development of machine learning algorithms for signal and image processing, with special attention to adaptive systems, neural networks and kernel methods. He conducts and supervises research on the application of these methods to remote sensing image analysis and recognition, and image denoising and coding. Dr Camps-Valls is the author (or co-author) of 50 papers in referred international journals, more than 70 international conference papers, 15 book chapters, and is editor of other related books, such as Kernel Methods in Bioengineering, Signal and Image Processing (IGI, 2007). He has served as reviewer to many international journals, and on the Program Committees of SPIE Europe, IGARSS, IWANN and ICIP. Dr Camps-Valls was a member of the European Network on Intelligent Technologies for Smart Adaptive Systems (EUNITE), and the Spanish Thematic Networks on 'Pattern Recognition' and 'Biomedical Engineering'. He is active in the R+D sector through a large number of projects funded by both public and industrial partners, both at national and international levels. He is an Evaluator of project proposals and scientific organizations. Since 2003 he has been a member of the IEEE and SPIE. Since 2009 he has been a member of the machine Learning for Signal Processing (MLSP) Technical Committee of the IEEE Signal Processing Society. Visit http://www.uv.es/gcamps for more information. Lorenzo Bruzzone received a laurea (M.S.) degree in electronic engineering (summa cum laude) ad a Ph.D. degree in telecommunications from the University of Genoa, Italy, in 1993 and 1998, respectively. From 1998 to 2000 he was a Postdoctoral researcher at the University of Genoa. In 2000 he joined the University of Trento, Italy, where he is currently a Full Professor telecommunications. He teaches remote sensing, pattern recognition, radar and electrical communications. Dr Bruzzone is the Head of the remote Sensing Laboratory in the Department of Information Engineering and Computer Science, University of Trento. His current research interests are in the area of remote-sensing image processing and recognition (analysis of multitemporal data, feature extraction and election, classification, regression and estimation, data fusion and machine learning). He conducts and supervises research on these topics within the frameworks of several national and international projects. He is an Evaluator of project proposals for many different governments (including the European Commission) and scientific organizations. He is the author (or co-author) of 74 scientific publication in referred international journals, more than 140 papers in conference proceedings and 7 book chapters. He is a referee for many international journals and has served on the Scientific Committees of several international conferences. He is a member of the Managing Committee of the Italian Inter-University Consortium on Telecommunications and a member of the Scientific Committee of the India-Italy Center for Advanced Research. Since 2009 he has been a member of the Administrative Committee of the IEEE Geoscience and Remote Sensing Society. Dr Bruzzone gained first place in the Student Prize Paper Competition of the 1998 IEEE International Geoscience and Remote Sensing Symposium (Seattle, July 1998). He was a recipient of the Recognition of IEEE Transactions on Geoscience and remote Sensing Best reviewers in 1999 and was a Guest Editor...
List of contents
About the editors.
List of authors.
Preface.
Acknowledgments.
List of symbols.
List of abbreviations.
I Introduction.
1 Machine learning techniques in remote sensing data analysis (Bjorn Waske, Mathieu Fauvel, Jon Atli Benediktsson and Jocelyn Chanussot).
1.1 Introduction.
1.2 Supervised classification: algorithms and applications.
1.3 Conclusion.
Acknowledgments.
References.
2 An introduction to kernel learning algorithms (Peter V. Gehler and Bernhard Scholkopf).
2.1 Introduction.
2.2 Kernels.
2.3 The representer theorem.
2.4 Learning with kernels.
2.5 Conclusion.
References.
II Supervised image classification.
3 The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data (J. Anthony Gualtieri).
3.1 Introduction.
3.2 Aspects of hyperspectral data and its acquisition.
3.3 Hyperspectral remote sensing and supervised classification.
3.4 Mathematical foundations of supervised classification.
3.5 From structural risk minimization to a support vector machine algorithm.
3.6 Benchmark hyperspectral data sets.
3.7 Results.
3.8 Using spatial coherence.
3.9 Why do SVMs perform better than other methods?
3.10 Conclusions.
References.
4 On training and evaluation of SVM for remote sensing applications (Giles M. Foody).
4.1 Introduction.
4.2 Classification for thematic mapping.
4.3 Overview of classification by a SVM.
4.4 Training stage.
4.5 Testing stage.
4.6 Conclusion.
Acknowledgments.
References.
5 Kernel Fisher's Discriminant with heterogeneous kernels (M. Murat Dundar and Glenn Fung).
5.1 Introduction.
5.2 Linear Fisher's Discriminant.
5.3 Kernel Fisher Discriminant.
5.4 Kernel Fisher's Discriminant with heterogeneous kernels.
5.5 Automatic kernel selection KFD algorithm.
5.6 Numerical results.
5.7 Conclusion.
References.
6 Multi-temporal image classification with kernels (Jordi Muñoz-Marí, Luis Gómez-Choa, Manel Martínez-Ramón, José Luis Rojo-Álvarez, Javier Calpe-Maravilla and Gustavo Camps-Valls).
6.1 Introduction.
6.2 Multi-temporal classification and change detection with kernels.
6.3 Contextual and multi-source data fusion with kernels.
6.4 Multi-temporal/-source urban monitoring.
6.5 Conclusions.
Acknowledgments.
References.
7 Target detection with kernels (Nasser M. Nasrabadi).
7.1 Introduction.
7.2 Kernel learning theory.
7.3 Linear subspace-based anomaly detectors and their kernel versions.
7.4 Results.
7.5 Conclusion.
References.
8 One-class SVMs for hyperspectral anomaly detection (Amit Banerjee, Philippe Burlina and Chris Diehl).
8.1 Introduction.
8.2 Deriving the SVDD.
8.3 SVDD function optimization.
8.4 SVDD algorithms for hyperspectral anomaly detection.
8.5 Experimental results.
8.6 Conclusions.
References.
III Semi-supervised image classification.
9 A domain adaptation SVM and a circular validation strategy for land-cover maps updating (Mattia Marconcini and Lorenzo Bruzzone).
9.1 Introduction.
9.2 Literature survey.
9.3 Proposed domain adaptation SVM.
9.4 Pro
Report
"The editors and the contributors have thought through how best to introduce the various topics and discussions relevant for remote sensing of data analysis and they do it convincingly and compellingly. Their book will deservedly become a proud possession for researchers in the field." (Current Engineering Practice, 1 November 2010)
